Search Results (542)

The face of cutting tools serves as the critical interface for chip–tool interaction and wear initiation, significantly influencing tool performance and service life. By implementing micro-groove structures on the face to reduce the chip–tool contact area, the cutting mechanics of the tool are altered. Theoretical analysis indicates that the cutting equations of the grooved tool have changed, with the modified tool exhibiting a larger shear angle compared to the original design. Finite element simulations and experiments demonstrate that grooved tool exhibit optimized cutting mechanics, characterized by a larger shear angle and improved edge sharpness. The shear angle of grooved tool is increased by about 3 degrees and the chip thickness is reduced by about 0.05 mm. Cutting tests confirm that the grooved tool reduces the main cutting force by more than 18%, with a smaller wear area on the face and improved wear conditions near the cutting edge. Due to materials such as stainless steel and titanium alloy, which have similar difficult-to-machine properties. The present results are based on AISI 201 and the specific groove geometry used in this study, and further work is required before generalizing to other difficult-to-cut materials and groove designs. In summary, based on the experimental data, the micro-groove cutting tool outperforms the original tool in terms of shear angle, cutting force, and durability. Specifically, the shear angle of the micro-groove cutting tool is larger, the cutting force is reduced, and the wear on the face is decreased. Full article

Background: Machine learning (ML) holds great promise for genomic breeding value prediction in livestock and poultry, yet its application in layer breeding remains limited. Methods: In this study, we used whole-genome resequencing data from 834 Wenshui Luhua Green-Shelled (WLGS) laying hens to predict genomic breeding values for eight egg production and egg quality traits using multilayer perceptron (MLP), random forest (RF), and genomic best linear unbiased prediction (GBLUP). Model performance was evaluated via 10-fold cross-validation, and the effects of data type and single nucleotide polymorphism (SNP) density were examined. Results: Heritability analysis indicated moderate heritability for egg number (EN) at 0.327. Egg weight-related traits (EW-30W, EW-40W, and EHD-40W) exhibited high heritability (0.570–0.631), while eggshell strength (ESS-40W) and thickness (EST-40W) showed moderate heritability at 0.228 and 0.220, respectively. Model comparisons revealed that RF performed best for egg shape index (ESI-30W, 0.395) and most egg quality traits, whereas GBLUP yielded optimal results for egg weight traits, achieving prediction accuracies of 0.392 for EW-30W and 0.432 for EW-40W. Whole-genome resequencing data consistently outperformed 50K chip data across all models, with GBLUP improving EW-40W prediction accuracy by 24.9%. SNP density analysis further showed that GBLUP remained stable under low-density conditions, while MLP and RF progressively improved with increasing density, with RF demonstrating the most pronounced advantage at high densities. Conclusions: In summary, the GBLUP model is suitable for traits with high heritability and low-density marker scenarios, while the RF model demonstrates significant predictive advantages for egg production and specific egg quality traits under high-density conditions. This study provides scientific basis for model selection in the genomic selection program for laying hens. Full article

To address the poor cutting stability and deterioration of hole quality caused by the inherent trade-off between chip evacuation performance and drill-body stiffness in U-drilling, a multi-objective optimization framework was established. The design variables were the core thicknesses ${\mathrm{L}}_1$ and ${\mathrm{L}}_2$ of the inner and outer chip flutes, the inner and outer offset angles ${\mathrm{\theta }}_1$ and ${\mathrm{\theta }}_2$, and the inner and outer helix angles ${\mathrm{\beta }}_1$ and ${\mathrm{\beta }}_2$. The objectives were to maximize the chip evacuation force and minimize the drill-body strain (which serves as an equivalent indicator of maximizing drill-body stiffness). The chip evacuation force was rapidly evaluated using a mechanistic chip evacuation force model derived from mechanism-based analysis. The drill-body strain was efficiently predicted using a GA–BP neural-network surrogate model. An NSGA-II algorithm combined with the entropy-weighted TOPSIS method was employed to solve the optimization problem, yielding the optimal parameter combination for the U-drill chip-flute geometry. The results show that drilling experiments on 42CrMo under the optimal structural parameter combination reduced the cutting forces in the $x$, $y$, and $z$ directions by approximately 11.2%, 13.1%, and 11.8%, respectively. The root-mean-square acceleration in the $x$ and $y$-directions decreased by about 17.3% and 22.9%, respectively. These improvements effectively enhanced the hole-wall surface roughness and hole diameter accuracy, and further improved chip evacuation smoothness and cutting stability of the U-drill. Full article

Ni thick films have a wide range of applications in mechanical areas for anti-corrosion, anti-friction and protection purposes, and are also extensively employed in the chip packaging field. Yet, the deposition of Ni thick films is still faced with many problems in deposition efficiency, dense structure and adhesion to the substrate. RF magnetron sputtering was employed to deposit on polished Ti substrate up to 10.8 µm thick Ni films at a high deposition rate (45 nm/min) in Ar atmosphere plus a small amount of H₂. Vacuum annealing was performed at 400 °C for 5 h. To characterize the adhesion via friction and scratch test, different loads were applied on both surfaces of as-sputtered and post-annealed Ni thick films, and results were comparatively analyzed. The films have high purity, compact structure, smooth surface and strong adhesion strength. Post-annealed samples showed better and stable adhesion of Ni thick films to the substrate surface. Full article

Conventional cooling schemes that rely on rigid heat-sink-to-die coupling in vertical stacks fail to track the dynamic, non-uniform heat map of high-performance artificial-intelligence (AI) chips employing chiplet-based heterogeneous integration, giving rise to local hot spots. To eliminate this mismatch, we present a leaf-vein-inspired fractal microchannel tailored for such AI processors. Its hierarchical bifurcation–confluence topology adaptively reshapes the flow field, delivering ultra-low thermal resistance, high heat-transfer coefficients, and uniform dissipation. Coupled with reconfigurable chiplet placement, the design is evaluated through FEM-based orthogonal experiments that rank the influence of coolant, channel diameter/depth, inlet/outlet position, substrate thickness, and flow rate via range analysis and Analysis of Variance (ANOVA). A machine-learned surrogate model of junction temperature is then fed to Particle Swarm Optimization (PSO) for multi-parameter optimization. When re-simulated with the optimal parameter set, the symmetric fractal network lowered the AI chip junction temperature from 127.80 °C to 30.97 °C, a 76% improvement, offering a theoretical basis for hotspot mitigation in advanced heterogeneous AI packages. Full article

Ultra-precision grinding is widely used in fields such as precision instrumentation, military industry, and aerospace. Focusing on a grinding system based on hydrostatic support, this paper investigates the formation mechanism and variation patterns of roundness error during grinding. The dynamic equations are derived based on the structural characteristics of a vertical grinding system. The uncut chip thickness is formulated, enabling the prediction of grinding forces through mathematical expressions. The dynamic equations are solved using a fully discrete algorithm to obtain the surface profile of the workpiece after machining, and roundness error is extracted using the least squares method. As the speed ratio of the grinding wheel to the workpiece increases, the grinding accuracy improves, and the roundness error can be controlled within 0.2 μm. The farther the grinding force application point is from the center of the slider, the greater the roundness error. Under the condition of meeting the processing range, a shorter grinding wheel contact rod should be selected. Full article

The micro-geometry of the cutting edge plays a crucial role in material flow ahead of the cutting edge and chip formation, primarily influencing chip formation mechanisms and the minimum cutting thickness. In the context of turning 304 stainless steel, however, existing research still lacks a unified quantitative framework linking “cutting edge micro-geometry—material separation behavior (separation point/minimum uncut chip thickness)—microstructural evolution of the machined surface.” This gap hampers mechanistic optimization design aimed at enhancing machining quality. This study examines the turning of 304 stainless steel by integrating analytical modeling, finite element simulation, and experimental validation to develop a predictive model for minimum cutting thickness. It analyzes the effects of tool nose radius and asymmetric edge morphology, and a microstructure evolution prediction subroutine is developed based on dislocation density theory. The results indicate that the minimum cutting thickness exhibits a positive correlation with the tool nose radius, and their ratio remains stable within the range of 0.25 to 0.30. Under asymmetric edge conditions, the minimum cutting thickness initially increases and then decreases as the K-factor varies. The developed subroutine, based on the dislocation density model, enables accurate prediction of dislocation density, grain size, and microhardness in the machined surface layer. Among the factors considered, the tool nose radius demonstrates the most pronounced influence on microstructure evolution. This research provides theoretical support and a technical reference for optimizing cutting-edge design and enhancing the machining quality of 304 stainless steel. Full article

Due to the unique microstructure and mechanical heterogeneity of austenitic stainless steel made via wire arc additive manufacturing (WAAM), its machinability differs significantly from that of rolled material. Accordingly, this study systematically investigates the influence of milling strategies on key process responses (cutting forces, surface roughness, vibration displacement, and temperature) to reveal the mechanisms of machining parameters during the milling of WAAM-fabricated austenitic stainless steel. The material used in this study is ER321 austenitic stainless steel. During deposition, the fusion zone cools more slowly than the transition zone; consequently, the fusion zone exhibits a hardness approximately 20 HV_0.1 lower than that of the transition zone. Surface roughness is primarily reduced by decreasing the primary feed per tooth. However, when the primary feed per tooth is small, ploughing is induced, which not only increases surface roughness by 25% but also causes abnormal increases in temperature and vibration displacement. Nevertheless, ploughing has little effect on the total milling force, and the feed per tooth shows a positive correlation with the total milling force. Tool run-out and an increase in the uncut chip thickness lead to a positive correlation between the radial depth of cut and the key process responses. Moreover, ploughing also occurs when the radial depth of cut is small. The axial depth of cut has almost no effect on the machining process. Moreover, a small-diameter mill leads to severe ploughing, and at a high table feed, climb milling leads to cutter offset. Full article

With the widespread application of high-power thick-film-substrate light-emitting diode (LED) packages, the performance of high-power LED modules has been continuously improved, making thermal management an increasingly critical issue. To enhance the heat dissipation performance of LED modules, this study investigates the effects of different heat dissipation structures on the temperature field using a finite element-based thermal simulation method, based on the thermal management enhancement characteristics of the LED. A thermal simulation model of the LED was established, and the thermal characteristics and temperature field characterization of its components were analyzed. Our results revealed significant temperature differences at various positions of the LED, particularly near the bottom surface of the heat sink and the contact surface with the LED chips, where the heat flux density exhibited notable variations. Properly adjusting the spacing between LEDs effectively reduced the maximum temperature of the module, with the optimal spacing determined to be approximately 19 mm. To further improve heat dissipation, pin-fin arrays were added to the heat sink, leading to a reduction of 8.79 K in the maximum temperature and 9.67 K in the minimum temperature of the LED module, which significantly enhanced the heat dissipation performance. The optimization measures effectively improved the temperature field characterization of the LED, contributing to enhanced performance and an extended lifespan of the LED module. Full article

This work presents an advanced electro-physical model for hydrogenated amorphous silicon (a-Si:H) Junction Field Effect Transistors (JFETs) to enable the design of devices with energy-efficient analog interface building blocks for Lab-on-Chip (LoC) systems. The presence of this device can support monolithic integration with thin-film sensors and circuit-level design through a validated compact formulation. The model accurately describes the behavior of a-Si:H JFETs addressing key physical phenomena, such as the channel thickness dependence on the gate-source voltage when the channel approaches full depletion. A comprehensive framework was developed, integrating experimental data and mathematical refinements to ensure robust predictions of JFET performance across operating regimes, including the transition toward full depletion and the associated current-limiting behavior. The model was validated through a broad set of fabricated devices, demonstrating excellent agreement with experimental data in both the linear and saturation regions. Specifically, the validation was carried out at 25 °C on 15 fabricated JFET configurations (12 nominally identical devices per configuration), using the mean characteristics of 9 devices with standard-deviation error bars. In the investigated bias range, the devices operate in a sub-µA regime (up to several hundred nA), which naturally supports µW-level dissipation for low-power interfaces. This work provides a compact, experimentally validated modeling basis for the design and optimization of a-Si:H JFET-based LoC front-end/readout circuits within technology-constrained and energy-efficient operating conditions. Full article

This study presents a unified parametric optimization framework for the thermal design of microchannel spreaders used in high-power processor cooling. The fin geometry is expressed in a shape-agnostic parametric form defined by fin thickness, top and bottom gap widths, and channel height, without prescribing a fixed cross-section. This approach accommodates practical fin profiles ranging from rectangular to tapered and V-shaped, allowing continuous geometric optimization within manufacturability and hydraulic limits. A coupled analytical–numerical model integrates conduction through the spreader base, interfacial resistance across the thermal interface material (TIM), and convection within the coolant channels while enforcing a pressure-drop constraint. The optimization uses a deterministic continuation method with smooth sigmoid mappings and penalty functions to maintain constraint satisfaction and stable convergence across the design space. The total thermal resistance ($R_{\mathrm{tot}}$) is minimized over spreader conductivities $k_s=400–2200$ W m⁻¹ K⁻¹ (copper to CVD diamond), inlet fluid velocities $U_{\mathrm{in}}=0.5–5.5$ m s⁻¹, maximum pressure drops of 10–50 kPa, and fluid pass counts $N_p\in \{1,2,3\}$. The resulting maps of optimized fin dimensions as functions of $k_s$ provide continuous design charts that clarify how material conductivity, flow rate, and pass configuration collectively determine the geometry, minimizing total thermal resistance, thereby reducing chip temperature rise for a given heat load. Full article

Pericarp characteristics are key factors determining the eating quality of fresh-eating maize. This study aimed to elucidate the genetic basis of traits such as pericarp thickness, break force, and brittleness in fresh-eating maize, identify key genes regulating these traits, and provide a theoretical foundation for improving mouthfeel quality through molecular marker-assisted breeding. Using 196 fresh-eating maize inbred lines with diverse genetic backgrounds, pericarp-related traits were phenotypically measured using a texture analyzer. Genotyping was performed using the GenoBaits Maize 45K Panel chip (MolBreeding, Shijiazhuang City, China). Genome-wide association studies (GWAS) were conducted to identify significantly associated SNP loci, and candidate genes were screened for functional annotation. Phenotypic analysis revealed a significant positive correlation between pericarp thickness and break force, and a significant negative correlation between break force and brittleness. GWAS detected 21, 2, and 1 stable SNPs significantly associated with pericarp thickness, break force, and brittleness, respectively. A total of 47 candidate genes for pericarp thickness, 7 for break force, and 4 for brittleness were identified. Functional annotation indicated that the candidate gene Zm00001eb314860 (ZmbZIP130), annotated as a member of the bZIP transcription factor family, may function as a pleiotropic gene involved in regulating pericarp-related traits. These findings demonstrate that pericarp traits in fresh-eating maize are controlled by multiple genes. The significant loci and candidate genes identified in this study lay a foundation for further elucidating the molecular mechanisms underlying pericarp quality formation and for molecular breeding. Full article

The rapid advancement of high-performance electronics has intensified the demand for wide-bandgap semiconductor materials capable of operating under high-power and high-temperature conditions. Among these, silicon carbide (SiC) has emerged as a leading candidate due to its superior thermal conductivity, chemical stability, and mechanical strength. However, the high cost and complexity of SiC wafer fabrication, particularly in slicing and exfoliation, remain significant barriers to its widespread adoption. Conventional methods such as wire sawing suffer from considerable kerf loss, surface damage, and residual stress, reducing material yield and compromising wafer quality. Additionally, techniques like smart-cut ion implantation, though capable of enabling thin-layer transfer, are limited by long thermal annealing durations and implantation-induced defects. To overcome these limitations, ultrafast laser-based processing methods, including laser slicing and stealth dicing (SD), have gained prominence as non-contact, high-precision alternatives for SiC wafer exfoliation. This review presents the current state of the art and recent advances in laser-based precision SiC wafer exfoliation processes. Laser slicing involves focusing femtosecond or picosecond pulses at a controlled depth parallel to the beam path, creating internal damage layers that facilitate kerf-free wafer separation. In contrast, stealth dicing employs laser-induced damage tracks perpendicular to the laser propagation direction for chip separation. These techniques significantly reduce material waste and enable precise control over wafer thickness. The review also reports that recent studies have further elucidated the mechanisms of laser–SiC interaction, revealing that femtosecond pulses offer high machining accuracy due to localized energy deposition, while picosecond lasers provide greater processing efficiency through multipoint refocusing but at the cost of increased amorphous defect formation. The review identifies multiphoton ionization, internal phase explosion, and thermal diffusion key phenomena that play critical roles in microcrack formation and structural modification during precision SiC wafer laser processing. Typical ultrafast-laser operating ranges include pulse durations from 120–450 fs (and up to 10 ps), pulse energies spanning 5–50 µJ, focal depths of 100–350 µm below the surface, scan speeds ranging from 0.05–10 mm/s, and track pitches commonly between 5–20 µm. In addition, the review provides quantitative anchors including representative wafer thicknesses (250–350 µm), typical laser-induced crack or modified-layer depths (10–40 µm and extending up to 400–488 µm for deep subsurface focusing), and slicing efficiencies derived from multi-layer scanning. The review concludes that these advancements, combined with ongoing progress in ultrafast laser technology, represent research opportunities and challenges in transformative shifts in SiC wafer fabrication, offering pathways to high-throughput, low-damage, and cost-effective production. This review highlights the comparative advantages of laser-based methods, identifies the research gaps, and outlines the challenges and opportunities for future research in laser processing for semiconductor applications. Full article

The investigation of topological structures and phases in photonics has created unprecedented opportunities for developing advanced on-chip terahertz waveguide devices. Topological waveguides, which exhibit reduced backscattering and improved turning characteristics, provide a potential route toward more compact and robust on-chip photonic systems. Unlike conventional waveguides, the mode fields in topological waveguides are localized at the domain wall interface and decay into the bulk, making their bending loss sensitive to both the bulk thickness and the photonic band gap. However, a comprehensive analysis that simultaneously considers the bulk thickness, photonic band gap, and bending loss remains lacking. In this paper, we comprehensively studied the relationship between the bending loss in valley Hall photonic crystal waveguides and both the bulk thickness and photonic band gap width, using an all-silicon terahertz platform. The results provide guidance and a reference for the routing and design of terahertz photonic systems. Full article

This study evaluates the surface roughness of micromilled 316L stainless steel parts fabricated via fused filament fabrication (FFF) and sintering, establishing statistical links between additive manufacturing and post-machining parameters. The surface roughness of the final part is affected by both 3D printing and micromachining parameters. The presented work has direct practical relevance because micromilled 316L stainless steel components are frequently used in applications such as lab-on-a-chip (LOC) devices and micro-electro-mechanical systems (MEMS), where fatigue behavior and the rheological behavior of fluid flow play critical roles. Both fluid flow and fatigue performance of micromilled components are highly dependent on surface integrity, including surface roughness, residual stresses, and microstructure. Specimens were produced using a 3D printer, under controlled layer thicknesses, raster angles, and fabrication directions, followed by a sintering process for the 3D-printed parts. The sintered parts are then micromilled at varying cutting directions (Angle Cut). Surface roughness (Ra) was measured with a profilometer, generating 34 experimental datasets analyzed through correlation and regression modeling. Cutting direction (Angle Cut) exhibited the strongest positive correlation with Ra (r = 0.486, p = 0.004), followed by layer thickness (r = 0.326, p = 0.060), whereas raster angle and fabrication direction had minimal influence. The multiple linear regression model accounted for 33.5% of Ra variance (R² = 0.335, p = 0.0158), highlighting that fine-layer deposition and alignment of tool paths with filament orientation significantly improve post-machined surface quality. Results confirm that additive-induced anisotropy persists after sintering, affecting chip formation and surface morphology during micromilling. The novelty of this work lies in its integrated hybrid framework, linking metal FFF process parameters, fabrication direction, and machining outcomes through a unified statistical approach. This foundation supports machine-learning-based prediction and hybrid process optimization in metal FFF systems, providing guidance for high-quality additive–subtractive manufacturing. Full article